基于特征选择与时间一致性稀疏外观模型的目标追踪算法

doi:10.16451/j.cnki.issn1003-6059.201803006

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摘要

为了更有效利用追踪目标的判别特征信息,提高目标追踪的精度和鲁棒性,在粒子滤波追踪框架下提出基于特征选择与时间一致性稀疏外观模型的目标追踪算法.首先,采集目标的正负模板和候选目标,根据特征选择模型对正负模板和候选目标进行特征选择,去除多余的干扰信息,得到关键的特征信息.然后,利用正负模板和候选目标的特征建立多任务稀疏表示模型,引入时间一致性正则项,促进更多的候选目标与先前帧的追踪结果具有稀疏表示的相似性.最后,求解多任务稀疏表示模型,得到判别稀疏相似图,获取每个候选目标的判别分,根据目标追踪结果更新正负模板.实验表明,即使在复杂的环境下,文中算法仍然比其它一些追踪算法具有更高的准确性.

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	张伟东
	赵建伟
	周正华
	曹飞龙

关键词 ：目标追踪, 稀疏表示, 特征选择, 时间一致性

Abstract：

To improve the tracking accuracy and the robustness of object tracking by exploiting discriminative features of a tracking target effectively, an object tracking algorithm is proposed in the framework of particle filter tracking based on the feature selection and temporal consistency sparse appearance model. Firstly,some positive templates,negative templates,and candidate targets are sampled, and their corresponding features are selected according to the feature selection model. The redundant interferential information is deleted, and the key feature information is obtained. Secondly, a multi-task sparse representation model containing a temporal consistency regular term is established via the features of positive templates,negative templates, and candidate targets. It induces more candidate targets to have sparse representation similarities with the previous tracking results. Thirdly,the multi-task sparse representation model is solved to gain the discriminative sparse similarity map, and the discriminative score is obtained for each candidate target. Finally, the positive templates and the negative templates are updated according to the tracking results. Experiments demonstrate that the proposed tracking algorithm produces better accuracy than some tracking methods,even under the complex environments.

Key words： Object Tracking Sparse Representation Feature Selection Temporal Consistency

收稿日期: 2017-10-23

ZTFLH:

TP 391

基金资助:

国家自然科学基金项目(No.61571410,61672477)、浙江省自然科学基金项目(No.LY18F020018)资助

通讯作者: 曹飞龙,博士,教授,主要研究方向为智能计算、图像处理等.E-mail:feilongcao@gmail.com.

作者简介: 张伟东,硕士研究生,主要研究方向为智能计算、图像处理等.E-mail:1178765538@qq.com.赵建伟,博士,教授,主要研究方向为智能计算、图像处理等.E-mail:zhaojw@amss. ac.cn.周正华,博士,副教授,主要研究方向为智能计算、图像处理等.E-mail:zzhzjw2003@163.com.

引用本文:

张伟东, 赵建伟, 周正华, 曹飞龙. 基于特征选择与时间一致性稀疏外观模型的目标追踪算法[J]. 模式识别与人工智能, 2018, 31(3): 245-255. ZHANG Weidong, ZHAO Jianwei, ZHOU Zhenghua, CAO Feilong. Object Tracking Algorithm Based on Feature Selection and Temporal Consistency Sparse Appearance Model. , 2018, 31(3): 245-255.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201803006 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I3/245

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